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2.
Application of Graphical Models in Protein-Protein Interactions and Dynamics
by Vajdi Hoojghan, Amir, Ph.D.  University of Massachusetts Boston. 2018: 92 pages; 10982841.
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Connection between Graphical Potential Games and Markov Random Fields with an Extension to Bayesian Networks
by Wang, Shiyun, M.S.  California State University, Long Beach. 2018: 43 pages; 10785804.
5.
Constructing a Graphical Model of the Drosophila melanogaster Metabolome
by Oza, Vishal H., Ph.D.  The University of Alabama. 2020: 416 pages; 27669307.
6.
Remote Homology Detection in Proteins Using Graphical Models
by Daniels, Noah Manus, Ph.D.  Tufts University. 2013: 122 pages; 3563611.
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Generative Probabilistic Models for Analysis of Communication Event Data with Applications to Email Behavior
by Navaroli, Nicholas Martin, Ph.D.  University of California, Irvine. 2014: 249 pages; 3668831.
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Networks of mixture blocks for non parametric Bayesian models with applications
by Porteous, Ian, Ph.D.  University of California, Irvine. 2010: 123 pages; 3403449.
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Representing Linguistic Knowledge with Probabilistic Models
by Meylan, Stephan Charles, Ph.D.  University of California, Berkeley. 2018: 206 pages; 10931065.
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Probabilistic learning for analysis of sensor-based human activity data
by Hutchins, Jonathan, Ph.D.  University of California, Irvine. 2010: 245 pages; 3432162.
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Bayesian generative modeling for complex dynamical systems
by Guan, Jinyan, Ph.D.  The University of Arizona. 2016: 120 pages; 10109036.
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Mind as Theory Engine: Causation, Explanation and Time
by Pacer, Michael D., Ph.D.  University of California, Berkeley. 2016: 380 pages; 10194103.
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PR-OWL Decision: A Framework for Decision Making with Probabilistic Ontologies
by Matsumoto, Shou, Ph.D.  George Mason University. 2019: 198 pages; 13864553.
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Learning and inference algorithms for dynamical system models of dextrous motion
by Varadarajan, Balakrishnan, Ph.D.  The Johns Hopkins University. 2011: 169 pages; 3496153.
18.
Probabilistic models of phase variables for visual representation and neural dynamics
by Cadieu, Charles, Ph.D.  University of California, Berkeley. 2009: 120 pages; 3402686.
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Efficient inference algorithms for near-deterministic systems
by Chatterjee, Shaunak, Ph.D.  University of California, Berkeley. 2013: 130 pages; 3616611.
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Data-driven computer vision for science and the humanities
by Lee, Stefan, Ph.D.  Indiana University. 2016: 130 pages; 10153534.
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Interactive Data Management and Data Analysis
by Yang, Ying, Ph.D.  State University of New York at Buffalo. 2017: 119 pages; 10288109.
25.
Beyond dynamic textures: A family of stochastic dynamical models for video with applications to computer vision
by Chan, Antoni Bert, Ph.D.  University of California, San Diego. 2008: 291 pages; 3331461.
26.
Modeling and Forecast of Brazilian Reservoir Inflows via Dynamic Linear Models under Climate Change Scenarios
by Lima, Luana Medeiros Marangon, Ph.D.  The University of Texas at Austin. 2011: 184 pages; 3530293.
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Controlling and evaluating inpainting with attentional models
by Ardis, Paul A., Ph.D.  University of Rochester. 2009: 136 pages; 3395371.
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Essays in the Dynamics Bayesian Models in Marketing
by Kim, Bumsoo, Ph.D.  The George Washington University. 2013: 100 pages; 3591693.
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Latent Variable Modeling for Networks and Text: Algorithms, Models and Evaluation Techniques
by Foulds, James Richard, Ph.D.  University of California, Irvine. 2014: 287 pages; 3631094.
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Two essays on “mining market basket data: Models and applications in marketing”
by Li, Xiaojun, Ph.D.  The George Washington University. 2008: 80 pages; 3315049.
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